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text-embedding

Text Embedding with Transformers

author: Jael Gu


Description

A text embedding operator takes a sentence, paragraph, or document in string as an input and outputs token embeddings which captures the input's core semantic elements. This operator is implemented with pre-trained models from Huggingface Transformers.


Code Example

Use the pre-trained model 'distilbert-base-cased' to generate a text embedding for the sentence "Hello, world.".

Write a pipeline with explicit inputs/outputs name specifications:

from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('text')
        .map('text', 'vec', ops.text_embedding.transformers(model_name='distilbert-base-cased'))
        .output('text', 'vec')
)

DataCollection(p('Hello, world.')).show()


Factory Constructor

Create the operator via the following factory method:

text_embedding.transformers(model_name=None)

Parameters:

model_name: str

The model name in string, defaults to None. If None, the operator will be initialized without specified model.

Supported model names:

Albert
  • albert-base-v1
  • albert-large-v1
  • albert-xlarge-v1
  • albert-xxlarge-v1
  • albert-base-v2
  • albert-large-v2
  • albert-xlarge-v2
  • albert-xxlarge-v2
Bart
  • facebook/bart-large
Bert
  • bert-base-cased
  • bert-base-uncased
  • bert-large-cased
  • bert-large-uncased
  • bert-base-multilingual-uncased
  • bert-base-multilingual-cased
  • bert-base-chinese
  • bert-base-german-cased
  • bert-large-uncased-whole-word-masking
  • bert-large-cased-whole-word-masking
  • bert-large-uncased-whole-word-masking-finetuned-squad
  • bert-large-cased-whole-word-masking-finetuned-squad
  • bert-base-cased-finetuned-mrpc
  • bert-base-german-dbmdz-cased
  • bert-base-german-dbmdz-uncased
  • cl-tohoku/bert-base-japanese-whole-word-masking
  • cl-tohoku/bert-base-japanese-char
  • cl-tohoku/bert-base-japanese-char-whole-word-masking
  • TurkuNLP/bert-base-finnish-cased-v1
  • TurkuNLP/bert-base-finnish-uncased-v1
  • wietsedv/bert-base-dutch-cased
BertGeneration
  • google/bert_for_seq_generation_L-24_bbc_encoder
BigBird
  • google/bigbird-roberta-base
  • google/bigbird-roberta-large
  • google/bigbird-base-trivia-itc
BigBirdPegasus
  • google/bigbird-pegasus-large-arxiv
  • google/bigbird-pegasus-large-pubmed
  • google/bigbird-pegasus-large-bigpatent
CamemBert
  • camembert-base
  • Musixmatch/umberto-commoncrawl-cased-v1
  • Musixmatch/umberto-wikipedia-uncased-v1
Canine
  • google/canine-s
  • google/canine-c
Convbert
  • YituTech/conv-bert-base
  • YituTech/conv-bert-medium-small
  • YituTech/conv-bert-small
CTRL - ctrl
DeBERTa
  • microsoft/deberta-base
  • microsoft/deberta-large
  • microsoft/deberta-xlarge
  • microsoft/deberta-base-mnli
  • microsoft/deberta-large-mnli
  • microsoft/deberta-xlarge-mnli
  • microsoft/deberta-v2-xlarge
  • microsoft/deberta-v2-xxlarge
  • microsoft/deberta-v2-xlarge-mnli
  • microsoft/deberta-v2-xxlarge-mnli
DistilBert
  • distilbert-base-uncased
  • distilbert-base-uncased-distilled-squad
  • distilbert-base-cased
  • distilbert-base-cased-distilled-squad
  • distilbert-base-german-cased
  • distilbert-base-multilingual-cased
  • distilbert-base-uncased-finetuned-sst-2-english
Electral
  • google/electra-small-generator
  • google/electra-base-generator
  • google/electra-large-generator
  • google/electra-small-discriminator
  • google/electra-base-discriminator
  • google/electra-large-discriminator
Flaubert
  • flaubert/flaubert_small_cased
  • flaubert/flaubert_base_uncased
  • flaubert/flaubert_base_cased
  • flaubert/flaubert_large_cased
FNet
  • google/fnet-base
  • google/fnet-large
FSMT
  • facebook/wmt19-ru-en
Funnel
  • funnel-transformer/small
  • funnel-transformer/small-base
  • funnel-transformer/medium
  • funnel-transformer/medium-base
  • funnel-transformer/intermediate
  • funnel-transformer/intermediate-base
  • funnel-transformer/large
  • funnel-transformer/large-base
  • funnel-transformer/xlarge-base
  • funnel-transformer/xlarge
GPT
  • openai-gpt
  • gpt2
  • gpt2-medium
  • gpt2-large
  • gpt2-xl
  • distilgpt2
  • EleutherAI/gpt-neo-1.3B
  • EleutherAI/gpt-j-6B
I-Bert
  • kssteven/ibert-roberta-base
LED
  • allenai/led-base-16384
MobileBert
  • google/mobilebert-uncased
MPNet
  • microsoft/mpnet-base
Nystromformer
  • uw-madison/nystromformer-512
Reformer
  • google/reformer-crime-and-punishment
Splinter
  • tau/splinter-base
  • tau/splinter-base-qass
  • tau/splinter-large
  • tau/splinter-large-qass
SqueezeBert
  • squeezebert/squeezebert-uncased
  • squeezebert/squeezebert-mnli
  • squeezebert/squeezebert-mnli-headless
TransfoXL
  • transfo-xl-wt103
XLM
  • xlm-mlm-en-2048
  • xlm-mlm-ende-1024
  • xlm-mlm-enfr-1024
  • xlm-mlm-enro-1024
  • xlm-mlm-tlm-xnli15-1024
  • xlm-mlm-xnli15-1024
  • xlm-clm-enfr-1024
  • xlm-clm-ende-1024
  • xlm-mlm-17-1280
  • xlm-mlm-100-1280
XLMRoberta
  • xlm-roberta-base
  • xlm-roberta-large
  • xlm-roberta-large-finetuned-conll02-dutch
  • xlm-roberta-large-finetuned-conll02-spanish
  • xlm-roberta-large-finetuned-conll03-english
  • xlm-roberta-large-finetuned-conll03-german
XLNet
  • xlnet-base-cased
  • xlnet-large-cased
Yoso
  • uw-madison/yoso-4096


checkpoint_path: str

The path to local checkpoint, defaults to None. If None, the operator will download and load pretrained model by model_name from Huggingface transformers.


tokenizer: object

The method to tokenize input text, defaults to None. If None, the operator will use default tokenizer by model_name from Huggingface transformers.


Interface

The operator takes a piece of text in string as input. It loads tokenizer and pre-trained model using model name. and then return text embedding(s) in ndarray.

__call__(txt)

Parameters:

data: Union[str, list]

​ The text in string or a list of texts. If data is string, the operator returns token embedding(s) in ndarray. If data is a list, the operator returns token embedding(s) in a list.

Returns:

numpy.ndarray or list

​ The text embedding (or token embeddings) extracted by model.


save_model(format='pytorch', path='default')

Save model to local with specified format.

Parameters:

format: str

​ The format of saved model, defaults to 'pytorch'.

path: str

​ The path where model is saved to. By default, it will save model to the operator directory.

from towhee import ops

op = ops.text_embedding.transformers(model_name='distilbert-base-cased').get_op()
op.save_model('onnx', 'test.onnx')

PosixPath('/Home/.towhee/operators/text-embedding/transformers/main/test.onnx')


supported_model_names(format=None)

Get a list of all supported model names or supported model names for specified model format.

Parameters:

format: str

​ The model format such as 'pytorch', 'torchscript'.

from towhee import ops


op = ops.text_embedding.transformers().get_op()
full_list = op.supported_model_names()
onnx_list = op.supported_model_names(format='onnx')
print(f'Onnx-support/Total Models: {len(onnx_list)}/{len(full_list)}')
2022-12-13 16:25:15,916 - 140704500614336 - auto_transformers.py-auto_transformers:68 - WARNING: The operator is initialized without specified model.
Onnx-support/Total Models: 111/126


Fine-tune

Requirement

If you want to train this operator, besides dependency in requirements.txt, you need install these dependencies.

! python -m pip install datasets evaluate scikit-learn

Get start

We have prepared some most typical use of finetune examples.

Simply speaking, you only need to construct an op instance and pass in some configurations to train the specified task.

import towhee

bert_op = towhee.ops.text_embedding.transformers(model_name='bert-base-uncased').get_op()
data_args = {
    'dataset_name': 'wikitext',
    'dataset_config_name': 'wikitext-2-raw-v1',
}
training_args = {
    'num_train_epochs': 3, # you can add epoch number to get a better metric.
    'per_device_train_batch_size': 8,
    'per_device_eval_batch_size': 8,
    'do_train': True,
    'do_eval': True,
    'output_dir': './tmp/test-mlm',
    'overwrite_output_dir': True
}
bert_op.train(task='mlm', data_args=data_args, training_args=training_args)

For more infos, refer to the examples.

Dive deep and customize your training

You can change the training script in your customer way. Or your can refer to the original hugging face transformers training examples.

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